{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Long-Horizon Probabilistic Forecasting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Long-horizon forecasting is challenging because of the *volatility* of the predictions and the *computational complexity*. To solve this problem we created the [NHITS](https://arxiv.org/abs/2201.12886) model and made the code available [NeuralForecast library](https://nixtla.github.io/neuralforecast/models.nhits.html). `NHITS` specializes its partial outputs in the different frequencies of the time series through hierarchical interpolation and multi-rate input processing. We model the target time-series with Student's t-distribution. The `NHITS` will output the distribution parameters for each timestamp. \n",
    "\n",
    "In this notebook we show how to use `NHITS` on the [ETTm2](https://github.com/zhouhaoyi/ETDataset) benchmark dataset for probabilistic forecasting. This data set includes data points for 2 Electricity Transformers at 2 stations, including load, oil temperature.\n",
    "\n",
    "We will show you how to load data, train, and perform automatic hyperparameter tuning, **to achieve SoTA performance**, outperforming even the latest Transformer architectures for a fraction of their computational cost (50x faster)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/LongHorizon_Probabilistic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast datasetsforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load ETTm2 Data\n",
    "\n",
    "The `LongHorizon` class will automatically download the complete ETTm2 dataset and process it.\n",
    "\n",
    "It return three Dataframes: `Y_df` contains the values for the target variables, `X_df` contains exogenous calendar features and `S_df` contains static features for each time-series (none for ETTm2). For this example we will only use `Y_df`.\n",
    "\n",
    "If you want to use your own data just replace `Y_df`. Be sure to use a long format and have a simmilar structure than our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datasetsforecast.long_horizon import LongHorizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>-0.041413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.185467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57600</th>\n",
       "      <td>HULL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>0.040104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57601</th>\n",
       "      <td>HULL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.214450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115200</th>\n",
       "      <td>LUFL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>0.695804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115201</th>\n",
       "      <td>LUFL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>0.434685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172800</th>\n",
       "      <td>LULL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>0.434430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172801</th>\n",
       "      <td>LULL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>0.428168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230400</th>\n",
       "      <td>MUFL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>-0.599211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230401</th>\n",
       "      <td>MUFL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.658068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288000</th>\n",
       "      <td>MULL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>-0.393536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288001</th>\n",
       "      <td>MULL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.659338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345600</th>\n",
       "      <td>OT</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>1.018032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345601</th>\n",
       "      <td>OT</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>0.980124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       unique_id                  ds         y\n",
       "0           HUFL 2016-07-01 00:00:00 -0.041413\n",
       "1           HUFL 2016-07-01 00:15:00 -0.185467\n",
       "57600       HULL 2016-07-01 00:00:00  0.040104\n",
       "57601       HULL 2016-07-01 00:15:00 -0.214450\n",
       "115200      LUFL 2016-07-01 00:00:00  0.695804\n",
       "115201      LUFL 2016-07-01 00:15:00  0.434685\n",
       "172800      LULL 2016-07-01 00:00:00  0.434430\n",
       "172801      LULL 2016-07-01 00:15:00  0.428168\n",
       "230400      MUFL 2016-07-01 00:00:00 -0.599211\n",
       "230401      MUFL 2016-07-01 00:15:00 -0.658068\n",
       "288000      MULL 2016-07-01 00:00:00 -0.393536\n",
       "288001      MULL 2016-07-01 00:15:00 -0.659338\n",
       "345600        OT 2016-07-01 00:00:00  1.018032\n",
       "345601        OT 2016-07-01 00:15:00  0.980124"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change this to your own data to try the model\n",
    "Y_df, _, _ = LongHorizon.load(directory='./', group='ETTm2')\n",
    "Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "\n",
    "# For this excercise we are going to take 960 timestamps as validation and test\n",
    "n_time = len(Y_df.ds.unique())\n",
    "val_size = 96*10\n",
    "test_size = 96*10\n",
    "\n",
    "Y_df.groupby('unique_id').head(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "DataFrames must include all `['unique_id', 'ds', 'y']` columns.\n",
    "Make sure `y` column does not have missing or non-numeric values. \n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, plot the `HUFL` variable marking the validation and train splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from utilsforecast.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_id = 'HUFL'\n",
    "fig = plot_series(Y_df, ids=[u_id])\n",
    "ax = fig.axes[0]\n",
    "\n",
    "x_plot = pd.to_datetime(Y_df[Y_df.unique_id==u_id].ds)\n",
    "y_plot = Y_df[Y_df.unique_id==u_id].y.values\n",
    "x_val = x_plot[n_time - val_size - test_size]\n",
    "x_test = x_plot[n_time - test_size]\n",
    "\n",
    "ax.axvline(x_val, color='black', linestyle='-.')\n",
    "ax.axvline(x_test, color='black', linestyle='-.')\n",
    "ax.text(x_val, 5, '  Validation', fontsize=12)\n",
    "ax.text(x_test, 3, '  Test', fontsize=12)\n",
    "fig"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Hyperparameter selection and forecasting\n",
    "\n",
    "The `AutoNHITS` class will automatically perform hyperparamter tunning using [Tune library](https://docs.ray.io/en/latest/tune/index.html), exploring a user-defined or default search space. Models are selected based on the error on a validation set and the best model is then stored and used during inference. \n",
    "\n",
    "The `AutoNHITS.default_config` attribute contains a suggested hyperparameter space. Here, we specify a different search space following the paper's hyperparameters. Notice that *1000 Stochastic Gradient Steps* are enough to achieve SoTA performance. Feel free to play around with this space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "import torch\n",
    "from neuralforecast.auto import AutoNHITS\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.losses.pytorch import DistributionLoss\n",
    "from ray import tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.WARNING)\n",
    "torch.set_float32_matmul_precision('high')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizon = 96 # 24hrs = 4 * 15 min.\n",
    "\n",
    "# Use your own config or AutoNHITS.default_config\n",
    "nhits_config = {\n",
    "       \"learning_rate\": tune.choice([1e-3]),                                     # Initial Learning rate\n",
    "       \"max_steps\": tune.choice([1000]),                                         # Number of SGD steps\n",
    "       \"input_size\": tune.choice([5 * horizon]),                                 # input_size = multiplier * horizon\n",
    "       \"batch_size\": tune.choice([7]),                                           # Number of series in windows\n",
    "       \"windows_batch_size\": tune.choice([256]),                                 # Number of windows in batch\n",
    "       \"n_pool_kernel_size\": tune.choice([[2, 2, 2], [16, 8, 1]]),               # MaxPool's Kernelsize\n",
    "       \"n_freq_downsample\": tune.choice([[168, 24, 1], [24, 12, 1], [1, 1, 1]]), # Interpolation expressivity ratios\n",
    "       \"activation\": tune.choice(['ReLU']),                                      # Type of non-linear activation\n",
    "       \"n_blocks\":  tune.choice([[1, 1, 1]]),                                    # Blocks per each 3 stacks\n",
    "       \"mlp_units\":  tune.choice([[[512, 512], [512, 512], [512, 512]]]),        # 2 512-Layers per block for each stack\n",
    "       \"interpolation_mode\": tune.choice(['linear']),                            # Type of multi-step interpolation\n",
    "       \"random_seed\": tune.randint(1, 10),\n",
    "       \"scaler_type\": tune.choice(['robust']),\n",
    "       \"val_check_steps\": tune.choice([100])\n",
    "    }"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "Refer to https://docs.ray.io/en/latest/tune/index.html for more information on the different space options, such as lists and continous intervals.m\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To instantiate `AutoNHITS` you need to define:\n",
    "\n",
    "* `h`: forecasting horizon\n",
    "* `loss`: training loss. Use the `DistributionLoss` to produce probabilistic forecasts.\n",
    "* `config`: hyperparameter search space. If `None`, the `AutoNHITS` class will use a pre-defined suggested hyperparameter space.\n",
    "* `num_samples`: number of configurations explored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [AutoNHITS(h=horizon,\n",
    "                    loss=DistributionLoss(distribution='StudentT', level=[80, 90]), \n",
    "                    config=nhits_config,\n",
    "                    num_samples=5)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model by instantiating a `NeuralForecast` object with the following required parameters:\n",
    "\n",
    "* `models`: a list of models.\n",
    "\n",
    "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit and predict\n",
    "nf = NeuralForecast(models=models, freq='15min')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `cross_validation` method allows you to simulate multiple historic forecasts, greatly simplifying pipelines by replacing for loops with `fit` and `predict` methods.\n",
    "\n",
    "With time series data, cross validation is done by defining a sliding window across the historical data and predicting the period following it. This form of cross validation allows us to arrive at a better estimation of our model’s predictive abilities across a wider range of temporal instances while also keeping the data in the training set contiguous as is required by our models.\n",
    "\n",
    "The `cross_validation` method will use the validation set for hyperparameter selection, and will then produce the forecasts for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "Y_hat_df = nf.cross_validation(df=Y_df, val_size=val_size,\n",
    "                               test_size=test_size, n_windows=None)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we merge the forecasts with the `Y_df` dataset and plot the forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>AutoNHITS</th>\n",
       "      <th>AutoNHITS-median</th>\n",
       "      <th>AutoNHITS-lo-90</th>\n",
       "      <th>AutoNHITS-lo-80</th>\n",
       "      <th>AutoNHITS-hi-80</th>\n",
       "      <th>AutoNHITS-hi-90</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-02-11 00:00:00</td>\n",
       "      <td>2018-02-10 23:45:00</td>\n",
       "      <td>-0.922304</td>\n",
       "      <td>-0.914175</td>\n",
       "      <td>-1.217987</td>\n",
       "      <td>-1.138274</td>\n",
       "      <td>-0.708157</td>\n",
       "      <td>-0.617799</td>\n",
       "      <td>-0.849571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-02-11 00:15:00</td>\n",
       "      <td>2018-02-10 23:45:00</td>\n",
       "      <td>-0.954299</td>\n",
       "      <td>-0.957198</td>\n",
       "      <td>-1.403932</td>\n",
       "      <td>-1.263984</td>\n",
       "      <td>-0.618467</td>\n",
       "      <td>-0.442688</td>\n",
       "      <td>-1.049700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-02-11 00:30:00</td>\n",
       "      <td>2018-02-10 23:45:00</td>\n",
       "      <td>-0.987538</td>\n",
       "      <td>-0.972558</td>\n",
       "      <td>-1.512509</td>\n",
       "      <td>-1.310191</td>\n",
       "      <td>-0.621673</td>\n",
       "      <td>-0.444359</td>\n",
       "      <td>-1.185730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-02-11 00:45:00</td>\n",
       "      <td>2018-02-10 23:45:00</td>\n",
       "      <td>-1.067760</td>\n",
       "      <td>-1.063188</td>\n",
       "      <td>-1.614276</td>\n",
       "      <td>-1.475302</td>\n",
       "      <td>-0.665729</td>\n",
       "      <td>-0.521775</td>\n",
       "      <td>-1.329785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-02-11 01:00:00</td>\n",
       "      <td>2018-02-10 23:45:00</td>\n",
       "      <td>-1.001276</td>\n",
       "      <td>-1.001494</td>\n",
       "      <td>-1.508795</td>\n",
       "      <td>-1.390156</td>\n",
       "      <td>-0.629212</td>\n",
       "      <td>-0.470608</td>\n",
       "      <td>-1.369715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581275</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 22:45:00</td>\n",
       "      <td>2018-02-19 23:45:00</td>\n",
       "      <td>-1.200041</td>\n",
       "      <td>-1.200862</td>\n",
       "      <td>-1.591271</td>\n",
       "      <td>-1.490571</td>\n",
       "      <td>-0.907190</td>\n",
       "      <td>-0.779424</td>\n",
       "      <td>-1.581325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581276</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:00:00</td>\n",
       "      <td>2018-02-19 23:45:00</td>\n",
       "      <td>-1.237206</td>\n",
       "      <td>-1.225333</td>\n",
       "      <td>-1.618691</td>\n",
       "      <td>-1.518204</td>\n",
       "      <td>-0.960075</td>\n",
       "      <td>-0.838512</td>\n",
       "      <td>-1.581325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581277</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:15:00</td>\n",
       "      <td>2018-02-19 23:45:00</td>\n",
       "      <td>-1.232434</td>\n",
       "      <td>-1.229675</td>\n",
       "      <td>-1.591164</td>\n",
       "      <td>-1.481251</td>\n",
       "      <td>-0.989993</td>\n",
       "      <td>-0.870404</td>\n",
       "      <td>-1.581325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581278</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:30:00</td>\n",
       "      <td>2018-02-19 23:45:00</td>\n",
       "      <td>-1.259237</td>\n",
       "      <td>-1.258848</td>\n",
       "      <td>-1.659239</td>\n",
       "      <td>-1.536979</td>\n",
       "      <td>-0.985581</td>\n",
       "      <td>-0.822370</td>\n",
       "      <td>-1.562328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581279</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:45:00</td>\n",
       "      <td>2018-02-19 23:45:00</td>\n",
       "      <td>-1.247161</td>\n",
       "      <td>-1.251899</td>\n",
       "      <td>-1.631909</td>\n",
       "      <td>-1.520350</td>\n",
       "      <td>-0.949529</td>\n",
       "      <td>-0.832602</td>\n",
       "      <td>-1.562328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>581280 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       unique_id                  ds              cutoff  AutoNHITS  \\\n",
       "0           HUFL 2018-02-11 00:00:00 2018-02-10 23:45:00  -0.922304   \n",
       "1           HUFL 2018-02-11 00:15:00 2018-02-10 23:45:00  -0.954299   \n",
       "2           HUFL 2018-02-11 00:30:00 2018-02-10 23:45:00  -0.987538   \n",
       "3           HUFL 2018-02-11 00:45:00 2018-02-10 23:45:00  -1.067760   \n",
       "4           HUFL 2018-02-11 01:00:00 2018-02-10 23:45:00  -1.001276   \n",
       "...          ...                 ...                 ...        ...   \n",
       "581275        OT 2018-02-20 22:45:00 2018-02-19 23:45:00  -1.200041   \n",
       "581276        OT 2018-02-20 23:00:00 2018-02-19 23:45:00  -1.237206   \n",
       "581277        OT 2018-02-20 23:15:00 2018-02-19 23:45:00  -1.232434   \n",
       "581278        OT 2018-02-20 23:30:00 2018-02-19 23:45:00  -1.259237   \n",
       "581279        OT 2018-02-20 23:45:00 2018-02-19 23:45:00  -1.247161   \n",
       "\n",
       "        AutoNHITS-median  AutoNHITS-lo-90  AutoNHITS-lo-80  AutoNHITS-hi-80  \\\n",
       "0              -0.914175        -1.217987        -1.138274        -0.708157   \n",
       "1              -0.957198        -1.403932        -1.263984        -0.618467   \n",
       "2              -0.972558        -1.512509        -1.310191        -0.621673   \n",
       "3              -1.063188        -1.614276        -1.475302        -0.665729   \n",
       "4              -1.001494        -1.508795        -1.390156        -0.629212   \n",
       "...                  ...              ...              ...              ...   \n",
       "581275         -1.200862        -1.591271        -1.490571        -0.907190   \n",
       "581276         -1.225333        -1.618691        -1.518204        -0.960075   \n",
       "581277         -1.229675        -1.591164        -1.481251        -0.989993   \n",
       "581278         -1.258848        -1.659239        -1.536979        -0.985581   \n",
       "581279         -1.251899        -1.631909        -1.520350        -0.949529   \n",
       "\n",
       "        AutoNHITS-hi-90         y  \n",
       "0             -0.617799 -0.849571  \n",
       "1             -0.442688 -1.049700  \n",
       "2             -0.444359 -1.185730  \n",
       "3             -0.521775 -1.329785  \n",
       "4             -0.470608 -1.369715  \n",
       "...                 ...       ...  \n",
       "581275        -0.779424 -1.581325  \n",
       "581276        -0.838512 -1.581325  \n",
       "581277        -0.870404 -1.581325  \n",
       "581278        -0.822370 -1.562328  \n",
       "581279        -0.832602 -1.562328  \n",
       "\n",
       "[581280 rows x 10 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_hat_df = Y_hat_df.reset_index(drop=True)\n",
    "Y_hat_df = Y_hat_df[(Y_hat_df['unique_id']=='OT') & (Y_hat_df['cutoff']=='2018-02-11 12:00:00')]\n",
    "Y_hat_df = Y_hat_df.drop(columns=['y','cutoff'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df = Y_df.merge(Y_hat_df, on=['unique_id','ds'], how='outer').tail(96*10+50+96*4).head(96*2+96*4)\n",
    "plot_series(forecasts_df=plot_df.drop(columns='AutoNHITS').rename(columns={'AutoNHITS-median': 'AutoNHITS'}), level=[90])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
